Analytical E-Commerce Processing System And Methods

ABSTRACT

A system and methods which enable modeling of end consumer interests based on online activity and producing e-commerce reports is described. The method includes scoring and classifying interests and preferences of consumers in relation to various items being offered as function of time and utilizing such scores to predict purchasing activity and revenue yield for n-dimensional combinations of interest for generation of consumer lists for target marketing and merchandising. The method also includes converse modeling of the performance and behavioral profile of items offered as a function of consumer activity. This Abstract is provided for the sole purpose of complying with the rules that allow a reader to quickly ascertain the subject matter of the disclosure contained herein. This Abstract is submitted with the explicit understanding that it will not be used to interpret or to limit the scope or the meaning of the claims.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional Applicationentitled “Analytical E-Commerce Processing System and Methods”.application Ser. No. 60/860,560, filed on Nov. 22, 2006, which is herebyincorporated by reference in its entirety. The present application isadditionally related to two co-pending U.S. patent applications entitled“Analytical E-Commerce Processing System and Methods”. ApplicationSerial Numbers are pending. These applications claiming priority to thesame provisional application and filed contemporaneously with thepresent application.

FIELD OF THE INVENTION

The present invention relates to consumer-based behavioral targetmarketing and merchandising in the context of item or product offeringsin e-commerce.

BACKGROUND OF THE INVENTION

Offering relevant products is becoming increasingly important fore-commerce companies in order for them to effectively attract and retainconsumers given the ever increasing number of competitors emerging onthe Internet. As consumers are faced with an overwhelming selection ofproducts, content, and/or service online, companies are faced with anequal level of decision complexity in order to effectively determinewhich of their ever expansive inventory of products should be offered toa consumer population the vast majority, of which are anonymous visitorsof their online stores. This lack of visibility into the interests andshopping preferences of a large and often heterogeneous consumer baseleads to suboptimal marketing and merchandising strategies as a resultof undifferentiated product offerings.

The economic implications of non-relevant product offerings are quiteconsiderable and could determine the long-term viability of presente-commerce companies engaged in a pernicious business cycle as they areforced to spend more on acquiring new customers in order to compensatefor their turnover of the consumers that have previously visited and mayhave purchased within their online stores.

The standard approaches used by e-commerce companies to target customersis based on multivariate analysis, segmentation, and list generation ofdemographic and psychographic data, preference data provided duringaccount registration online and/or historic purchase data of individualusers in standard data-mart/data-warehouse environments. Each of thesecriteria presents significant limitations in enabling effective andscalable targeting of online consumers. First, demographic andpsychographic data offers poor resolution into the nuanced interests ofcustomers to specific products or product classes within a wide array ofhighly diversified inventories. In addition, only the disproportionatelysmall population of consumers that have provided their identifiableaddress information (i.e., buyers, registrants/account holders, etc.)can be classified based on these criteria and thus targeted. The vastmajority of online shoppers, who are anonymous visitors, simply can notbe targeted.

Second, in the case of the use of interest or preference data explicitlyprovided by online consumers when they register or create accounts, suchdata is often sparse and unreliable in determining a customer's trueshopping interests. It is usually non-reflective on what a particularcustomer has actually purchased, if they have purchased at all. This issimilar in many ways to the demographic and psychographic data which haslimited consumer reach and allows for targeting of disproportionatelysmall populations.

Lastly, the third criteria for targeting consumers considered mosteffective by traditional brick-and-mortar companies and optimized, inparticular, by retail catalog companies, is data on historic purchasingactivity. While initial purchasing activity is an often effectivedeterminant of future purchasing activity, it is dependent on the typeof product being offered and their natural buying cycles (i.e.,refrigerators and mortgage packages versus groceries and DVDs, etc.).Such factors determine the likelihood of repeat purchase rates. Responserates often drop precipitously on the second and future campaigns asnatural buying thresholds have been exceeded.

Analysis of order data has been the mainstay of current databasemarketing/business intelligence technologies due in large part to itssuccess in traditional catalog retail business models. When applied toe-commerce, the use of an order-centric data model, as typified in thecanonical data warehouse star-schemas, presents significant limitationsas an artifact of an old world brick-and-mortar paradigm. Withpoint-of-sale systems such as cash registers as the primarytransactional system of record, purchasing activity has been the centralevent space for analysis by commercial consumer oriented databasesystems offering a very myopic view of the breadth of important shoppingdynamics that are occurring.

Despite the emergence of e-commerce and its vast new sources oftransactional data, the capacity of e-commerce companies to effectivelysegment and target market and merchandise to their customers hasremained a considerable challenge. Of the many reasons why efficient useof clickstream data has remained elusive for e-commerce companies, themost noteworthy data management and analytical limitations include:unwieldy volumes (terabytes) of raw transactional data requiring highstorage and processing capacity, non-standardized data structuresleading to limited semantic resolution and join complexity from modelingof multiple and heterogeneous event spaces due to dimensionalnon-conformity, and disproportionately small population of knownconsumers such as buyers and registrants, that are often considered morevaluable to companies, whose clickstream data can actually be applied tothem and be effectively leveraged to increase revenue and profits.

Many current solutions in the market have developed approaches tointegrating voluminous clickstream data but still offer little to noimproved ability to effectively target their consumers in order toincrease revenues and profits. Many of the packaged data warehousesolutions, while integrating clickstream data, have architected theschema based on traditional approaches that make multivariate analysisacross single or the desired multiple events inordinately processingintensive and often improbable to conduct. Given the cost of storing andprocessing terabytes of raw clickstream data, such packaged solutionsare still oriented towards standard order-centric schemas and dataarchitectures.

To fulfill this growing need to store and process terabytes ofclickstream data in a cost-effective manner for e-commerce companies,web analytic service companies emerged. Many of these companies serveostensibly as outsourced data warehouse solutions for e-commercecompanies. Their technology services allow for the rapid processing ofclickstream data in order to provide reports for aggregate trafficanalysis, page performance, site usage, and conversion analysis. Rarelycan such patterns give insight into meaningful shopping patterns thatcan be readily attributed to individual or segments of customers fortarget marketing and merchandising.

Only recently, and in rare cases, are the client company's internalcustomer ids provided to such third party analytic services to allow fortrue onsite behavioral mapping and identification. The emergence ofcustomer-level clickpath aggregation has led to new technologies inpartnership with Email Service Providers whereby client companies canset up specific business rules to instantiate automated targetingevents. The best known involves the use of trigger-based events whereconsumers that exhibit specified actions online (i.e., abandon item inshopping cart, download article, etc.) are sent a targeted emailrelating to the event in order to influence a desired activity such as apurchase or subscription.

Despite major advancements in processing power and storage capacity,most commerce analytic data systems (i.e., data warehouses, data marts,etc.) fail to provide companies with the ability to determine and launchhigh-performance campaigns by effectively determining what to offertheir fickle and largely anonymous mass of customers as well as themeans of targeting them in the rare occasions that their interests andpreferences are determined. The analytic limitations of current directmarketing and merchandising technology solutions are the result of thecontinued use of an increasingly outdated commerce data model paradigm,inherent in brick-and-mortar systems, which are primarily designed tomine order-centric activity, albeit across a limitless set ofdimensions.

Given the aforementioned, a need exists for decision support/revenuemanagement system that effectively models the full breadth and depth ofe-commerce data to enable companies to optimize servicing of theircustomers based on revenue projections of their differentiated shoppingbehaviors.

SUMMARY OF THE INVENTION

The present invention provides a system, methods, and computer programwhich enables users to model end consumer interests in items based onexhibited shopping activity online in order to predict purchasingpatterns and revenue yield is described.

One embodiment provides a method that includes deriving amulti-dimensional, multi-resolutional, de-normalized interaction tableand populating the table with information such as information onmerchandize in the form of apparel, content, multi-media files, consumergoods, services, offerings and the like. One provided method thenderives an e-commerce report from the table.

Another embodiment provides a computing apparatus including a processor,memory, and a storage medium. The storage medium contains a set ofprocessor executable instructions that, when executed by the processorconfigure the computing apparatus to derive a multi-dimensional,multi-resolutional, de-normalized interaction table and populate thetable with information such as, information on merchandize in the formof apparel, content, multi-media files, consumer goods, services,offerings and the like. One provided computing apparatus is configuredby the instructions to derive an e-commerce report from the table.

A further embodiment of a provided computer software product includes astorage medium containing a set of processor executable instructionsthat, when executed by a processor, configure a computing apparatus toderive a multi-dimensional, multi-resolutional, de-normalizedinteraction table and populating the table with information such asinformation on merchandize in the form of apparel, content, multi-mediafiles, consumer goods, services, offerings and the like. One providedsoftware product further configures the computing apparatus to derive ane-commerce report from the table.

Another embodiment of a provided method includes modeling an aggregateset of affinity scores from a plurality of information such asinformation on products or services at varying resolutions, informationon potential customers, and information on events at varying times. Themethod calculates a buying probability from at least one affinity scorefrom the set, and then produces an e-commerce report from the buyingprobability.

Another embodiment of a provided computing apparatus includes aprocessor, a memory, and a storage medium. The storage medium contains aset of processor executable instructions that, when executed by aprocessor configure the computing apparatus to model a aggregate set ofaffinity scores from a plurality of information, the information mayinclude, but is not limited to, information on products at variousresolutions, information on potential customers, and information relatedto various events. The computing apparatus is further configured tocalculate a buying probability from at least one of the affinity scoresand produce an e-commerce report from the affinity scores.

A still further embodiment of a provided computer software productincludes a computer readable medium containing a set of processorexecutable instructions that, when executed by a processor configure thecomputing apparatus to model a aggregate set of affinity scores from aplurality of information, the information may include but is not limitedto information on products at various resolutions, information onpotential customers, and information related to various events. Thecomputing apparatus is further configured to calculate a buyingprobability from at least one of the affinity scores and produce ane-commerce report from the affinity scores.

Further embodiments provide methods, computing apparatus and softwareproducts for implicitly scoring and classifying the interests andpreferences of consumers in relation to various dimensions of itemsbeing offered (i.e., products, content, service packages, etc.) asfunction of time and utilizing such scores to predict purchasingactivity and forecast revenue yield for n-dimensional combinations ofinterest for optimal generation of consumer lists for target marketingand merchandising. The method may also include converse modeling of theperformance and behavioral profile of items offered as a function ofconsumer activity.

According to aspects of the present invention, a database schema forimplicitly determining the interests of customers and predicting theirbuying patterns and revenue yield for various aspects of specified itemswith which they have interacted online, is disclosed.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present invention taught herein areillustrated by way of example, and not by way of limitation, in thefigures of the accompanying drawings, in which:

FIG. 1 is an illustration of a customer-centric targeting systemconsistent with one embodiment of the present invention;

FIG. 2 is an illustration of a product-centric targeting systemconsistent with one embodiment of the present invention;

FIG. 3 is an illustration of customer and product attribute data modelconsistent with one embodiment of the present invention;

FIG. 4 is an illustration of potential targeting model consistent withone embodiment of the present invention;

FIG. 5 is an illustration of an established targeting model consistentwith one embodiment of the present invention;

FIG. 6 is an illustration of a standard commerce analytic data model;

FIG. 7 is an illustration of a standard multidimensional commerceanalytic data model consistent with one embodiment of the presentinvention;

FIG. 8 is an illustration of a multi-event commerce data modelconsistent with one embodiment of the present invention;

FIG. 9 is an illustration of a targeting limitation of demo/psycho andorder-based customer attributes system consistent with one embodiment ofthe present invention;

FIG. 10 is an illustration of the limitations of current commerce datamodel consistent with one embodiment of the present invention;

FIG. 11 is an illustration of a multi-event clickstream commerce datamodel consistent with one embodiment of the present invention;

FIG. 12 is an illustration of an order-centric commerce model consistentwith one embodiment of the present invention;

FIG. 13 is an illustration of a multi-event clickstream commerce datamodel consistent with one embodiment of the present invention;

FIG. 14 is an illustration of an implicit shopping process consistentwith one embodiment of the present invention;

FIG. 15 shows a shopping model illustrating conditional shopping logicconsistent with one embodiment of the present invention;

FIG. 16 is an illustration of an E-commerce shopping model consistentwith one embodiment of the present invention;

FIG. 17 is an illustration of a interaction event consistent with oneembodiment of the present invention;

FIG. 18 is an illustration of shopping “friction points” consistent withone embodiment of the present invention;

FIG. 19 is an illustration of exhibited behavior and interventionstrategy consistent with one embodiment of the present invention;

FIG. 20 is an illustration of a behavior model consistent with oneembodiment of the present invention;

FIG. 21 is an illustration of a nuance analytics data flow systemarchitecture consistent with one embodiment of the present invention;

FIG. 22 is an illustration of a clickstream data source extractionmodalities consistent with one embodiment of the present invention;

FIG. 23 illustrates a method of generation of customer target listsconsistent with various embodiments of the present invention;

FIG. 24 further illustrates the construction of an exemplary schema;

FIG. 25 illustrates an exemplary calculation of affinity scores;

FIG. 26 illustrates product-centric multivariate buying probabilities;

FIG. 27 further illustrates the customer-centric multivariate buyingprobabilities;

FIG. 28 is an illustration of generation of multivariate customer targetlists consistent with one embodiment of the present invention;

FIG. 29 is an illustration of a local analytical processing systemconsistent with one embodiment of the present invention;

FIG. 30 illustrates a computing device and software product consistentwith various provided embodiments;

FIG. 31 illustrates a method consistent with provided embodiments;

FIG. 32 illustrates another method consistent with provided embodiments;

FIG. 33 illustrates another method consistent with provided embodiments;and

FIG. 34 illustrates another method of generation of customer targetlists consistent with various embodiments of the present invention.

It will be recognized that some or all of the Figures are schematicrepresentations for purposes of illustration and do not necessarilydepict the actual relative sizes or locations of the elements shown. TheFigures are provided for the purpose of illustrating one or moreembodiments of the invention with the explicit understanding that theywill not be used to limit the scope or the meaning of the claims.

DETAILED DESCRIPTION OF THE INVENTION

In the following paragraphs, the present invention will be described indetail by way of example with reference to the attached drawings. Whilethis invention is capable of embodiment in many different forms, thereis shown in the drawings and will herein be described in detail specificembodiments, with the understanding that the present disclosure is to beconsidered as an example of the principles of the invention and notintended to limit the invention to the specific embodiments shown anddescribed. That is, throughout this description, the embodiments andexamples shown should be considered as exemplars, rather than aslimitations on the present invention. Descriptions of well knowncomponents, methods and/or processing techniques are omitted so as tonot unnecessarily obscure the invention. As used herein, the “presentinvention” refers to any one of the embodiments of the inventiondescribed herein, and any equivalents. Furthermore, reference to variousfeature(s) of the “present invention” throughout this document does notmean that all claimed embodiments or methods must include the referencedfeature(s).

Online commerce presents a unique set of marketing difficulties andopportunities that are not usually present in the traditional“brick-and-mortar” commercial operation. In an online environment usersmay typically be relatively anonymous and very little demographicinformation may be known about the user. The users, or potentialcustomers, may browse the site from a wide range of geographical places.Typical use data collection methods can result in very large data setsthat present significant difficulties in categorizing and processingmeaningful information from the collected data.

Despite major advancements in processing power and storage capacity,most commerce analytic data systems (i.e., data warehouses, data marts,etc.) fail to provide companies with the ability to determine and launchhigh-performance campaigns by effectively determining what to offertheir fickle and largely anonymous mass of customers, as well as themeans of targeting them in the rare occasions that their interests andpreferences are determined. The analytic limitations of current directmarketing and merchandising technology solutions are the result of thecontinued use of an increasingly outdated commerce data model paradigm,inherent in brick-and-mortar systems, which are primarily designed tomine order-centric activity, albeit across a limitless set ofdimensions.

A significant key to unlocking economic benefits of personalization andtarget marketing lies beyond improvements in data storage capacityand/or expedient data processing technologies. Instead, as evidenced bythe new data modeling approach consistent with a method provided, theability to significantly increase purchasing activity requiresfundamental shifts in the criteria (attributes/dimension) that companiesuse to target their customers. Various embodiments of the presentinvention provide methods of behavioral targeting of potential customersthrough an analytical e-commerce engine.

Utilizing various embodiments of the methods provided an online storecan unleash the major revenue-generating potential of increasing orderconversion rates by significantly increasing the target population ofcustomers based on their interests out of the massive untapped market of“shoppers”, the vast majority of which have never bought. One methodprovided herein, may calculate the dynamic “interests” of ever-shiftingcustomers based on their “interaction” with equally dynamic merchandiseor content.

The expansion of the event space between the product, customer, and timedimension to include a broad set of distinct shopping events(represented as fact tables) beyond the standard order-event, is acentral aspect of the advanced capabilities of various providedembodiments. By prior semantic enrichment and assembly of distinctiveshopping events found in raw clickstream data into a unified shoppingbehavioral ontology, a single multi-event “intelligent” fact table maybe created that can enable new and significantly faster calculations ofcustomers' interests across numerous dimensions. Potential customersfuture purchasing activity may be modeled advertising campaign revenuepotential calculated, and e-commerce reports such as target advertisinglist may be immediately generated for such campaigns.

In one embodiment, a system is configured to determine the interests ofpotential customers by calculating their interests and/or preferencesscores in relation to its merchandise set through high throughputmultidimensional modeling of their historic clickstream activity. Thesystem may then use these dynamic and adaptive customer interest scoresfor advanced behavior-based segmentation. The segmentation may be usedto create predictive parameters to forecast buying probabilities of aparticular product. Additionally, the disclosed methods may calculatethe likely sales performance of a nearly limitless set of possibletargeting/personalization advertising campaigns with the application ofHierarchical Bayesian techniques. Given the immense predictive capacityof these novel multidimensional interest scores, the system immediatelyhelps business users develop the most profitable campaigns, byidentifying and generating optimal target populations (customer lists)of high-performing prospects. In one embodiment a specialized GUI mayallow a user to configure an ad campaign from the data models.

The multidimensional nature provided by various embodiments, allowsuniquely powerful analytic flexibility in terms of generating e-commercereports, such as advertising campaigns and promotions from two majorbusiness exploratory modalities:

-   -   1. Product-centric modality: determination of which customer        (group of customers) should be targeted with a known product        (group of products).    -   2. Customer-centric modality: determination of what product        (group of products) to offer a known customer (group of        customers).

Often in the case of the business user (merchandiser) that has aparticular product or conceptual classification of products in mind thatthey are looking to sell (i.e., sku, Brand, Category, Dresses over $300,Brown shoes in size 4 on sale last week, for example), the system can beused to determine who the best prospects are for selective targeting atvarious levels of interest for the option of further micro-segmentation.

In the case of the business user (marketer) a provided software productcan be used to determine what the most appealing products or sets ofproducts are for selective targeting at various levels of interest forthe option of further micro-segmentation. This allows a select segmentof customers or conceptual classification of customers (i.e., NewCustomers, Holiday Visitors that never bought, Price-Insensitive ShoeBuyers, Weekend Shoppers, etc.) to be targeted with advertisingcustomized to their preferences. Thus by targeting customers based ontheir interest level, a much wider audience of prospects isautomatically generated which leads to significantly higher conversionand sales.

One objective of a system configured consistent with embodiments of thepresent invention is to increase order conversion rates and profitmargins of marketing campaigns and promotions by determining the rightproducts to offer to the right customers at the right time. The methodsprovided by various embodiments of the present invention were developedfor use a myriad of enterprise systems as well as across various domainsand sectors such as retail, travel, literature and publishing, finance,politics, and education to name a few.

One feature of a method provided herein is that it capitalizes on therelationship between two entities, customers and products, and someevent between them, most often, a purchase. While business userstypically do not consider these two entities in strong relation to eachother, given the commonplace vertical silos of marketing andmerchandising departments within retail companies, a stringentunderstanding of the relationship between the two is critical tooptimizing sales and is thus at the center of the method's capability.

One embodiment of a method addresses both customer-centric targetingaspects, illustrated in FIG. 1, and product-centric targeting,illustrated in FIG. 2. In the customer-centric model, a customer 10 isrelated to a plurality of products 20. As used herein “product” or“merchandize” may comprise tangible products such as clothing,intangible products such as multi-media files, and services. Theproduct-centric targeting illustrated in FIG. 2 relates a specificproduct 20 to a plurality of customers 10. A central challenge, and thusopportunity, facing all retail businesses is trying determine theprofitable circumstances for which products should be to offered tocustomers and which customers to offer them to. A customer-centric modelseeks to determine which products, and or services to offer customers. Aproduct-centric model seeks to determine which potential customer shouldbe target with which range of products.

As illustrated in FIG. 3, an embodiment of the present invention takesadvantage of the understanding various customer attributes 30, such asplace of residence, age, gender, interests, spending habits, etc., mayimprove the determination of what should be offered and thus what he/sheis likely to purchase. Similarly, an improved understanding of productattributes 40 such as price, color, size range, brand, category,shelf-speed, rate to clearance, etc., informs the decision of theconditions under which it should be sold and likely to whom in order tomaximize the possibility of a sale. As illustrated, many of theseattributes are at resolutions that differ from one another. For example,price may be in dollars while shelf-speed may be expressed in days,weeks, or other time periods.

Consistent with aspects of the present invention, improvements inselling dynamics are provided from differentiation in the attributesamongst customers and establishing correlations with potentialdifferentiation within products. The importance of such attributecorrelation patterns, is exemplified in the most basic retail businessmodels. These are often typified by the knowledge and the ability toimmediately capitalize on such knowledge by the local storekeeper. Thestorekeeper's inferred determination of the various preferences andinterests of customers to the qualities/characteristics of productsbased on observed transactions can be used to optimize sales. Inessence, the storekeeper, can establish important relationships betweenvarious segments of customers with various segments of products based onimplicit correlations amongst their respective sets of attributes basedon specific types of transactions. As demonstrated by FIG. 4 and FIG. 5,specific relationships can be observed between customer attributes 30and product attributes 40 within a particular event space. The eventspace can include purchase related transactions, and provide the basicframework for a potential marketing model. For example, if there is apurchase correlation between the State and Brand attributes of thecustomer and product, where customers from Connecticut may have asignificant proclivity to buy Helen Wang. See FIG. 5.

The efficacy of customer attribute 30 and product attribute 40 data indriving one-to-one or targeted marketing, can prove increasinglydifficult when dealing with the enormous scale of e-commerce. In moste-commerce environments, many of the millions of customers 10 may remainanonymous. These individuals are constantly choosing from an equallyexpansive breadth of products 20 being offered under a wide array ofpurchasing dynamic options. As such, increased emphasis has been placedon developing techniques that allow companies to improve sales andprofits by effectively targeting their customers with the productsand/or services which they are most interested in and thus likely topurchase. One feature of various embodiments is that they providemethods of correlating these attributes.

In the early phase of e-commerce this notion of one-to-oneindividualized marketing and personalized merchandising seemedincreasingly likely due to the ubiquity of transactional and consumerdata, in particular, data being collected about all of the pages onwebsites that users were continuously clicking on and viewing. Knownotherwise as clickstream data, this data is generated by webserversabout a user's browsing activity. Clickstream data can provide immediateinsight into aggregate traffic activity to see which areas of thewebsite are being most visited and/or under utilized. The presentinvention provides more advanced applications of clickstream data byprocessing it and associating it with individual customers and theirpreferences thus providing powerful personalized marketing opportunitiesthat lead to individualized or segment-targeted email campaigns orversions of the website.

Despite the significant investments in technical solutions, the promiseof personalization has never been realized. Significant advancementshave been made in the collection, storage, and, in certain ways,application of advanced statistical data mining techniques to increasethe yield of potentially beneficial models. The limited success of thesefirst-generation personalization technologies, may have been dependenton a major oversight in the assembly and architecture of the underlyingdata in these analytic database systems. In short, the current datamodel, illustrated in FIG. 6, prevalent in nearly all commercialanalytic database applications, has become largely obsolete as ananachronism of the order-centric world of traditional brick-and-mortarcommerce for which it was designed.

As illustrated above in FIG. 6, the data architected and modeled is areflection of the business model often inheriting all of the importantnuances and caveats of the transactional dynamics. In this illustration,transactions such as orders, may be stored in “Online TransactionProcessing” (OLTP) database 50 and data models 70 may be stored in an“Online Analytical Processing” (OLAP) database 60. These dynamicsinclude pricing promotions and seasonal sales strategies in the case ofwell designed analytic data models.

In its most basic formulation, the data model 70, illustrated in FIG. 7,represents the relationship between different entities that can berepresented as dimensional tables within specific event spaces oractions typically represented as fact tables. Thus, the central event incommerce being the Order, all aspects/dimensions of this event'soccurrence are incorporated into the model for analysis. In addition,other important dimensions such a Time/Date, Promotion/Campaign, etc.are often included as part of the standard commerce data model 70. Theinclusion of numerous dimensional tables with their own extensive set ofattributes/dimensions in a star-schema data model 80 allows for theexpedient exploratory analysis across a massive array of variousn-dimensional combinations across specific events.

The star-schema data model 80, illustrated in FIG. 7, has proveninvaluable in the wide-scale commercial application of data warehouseand data mart technologies that are reliant on the intelligentaggregation of key commerce metrics such as sales, profits, grossmargin, volume, average order size, and the like, across a number ofdimensional combinations across customer groups, product types,seasonality and time, and strategic campaigns and promotions to producevaluable and mission-critical information in a rapid time frame. Whileits denormalized architecture leads to significant storage costs, thisfacet is what allows OLAP databases 60 to produce rapid results withminimal joins across extremely large historical tables of data.

In contrast, standard commercial OLTP systems are usually characterizedby a large number of relatively simple queries on a reduced data set.Typical OLAP architectures apply complex transformation rules onvoluminous amount of data. OLAP tools may have different databasearchitectures (Relational OLAP, Multi-dimensional OLAP, . . . ) forstoring information. As well OLAP access may be performed throughdifferent types of data architecture distribution (i.e., centralizedOLAP database or distributed OLAP databases). Clickstream databasesystems additionally provide such transactional data which may includepurchasing data and in the case of the latter high volumes of page viewactivity. The dimensional data pertains to the attributes of a givenevent such as the consumer, item, and time at which it occurred. Suchdata can be found in OLTP and OLAP data systems.

Often, as illustrated in FIG. 8, companies will often extend thestar-schema data model 80 to incorporate additional critical entitiesand types of events that reflect the complexities of their own businessmodel in order to uncover important dynamics to drive sales and profits.The inclusion of additional tables, while certainly causing performancehits due to increased storage and processing time through increased datanormalization, provide immense economic benefits that far outweigh thesetechnical costs.

One advantage of methods of the present invention lies in their abilityto incorporate additional events and dimensions to provide even greateranalytical insight through increased resolution of transactionalactivity to yield potentially important patterns. Many of these eventsmay be present in clickstream while others may be derived events. Giventhis immensely flexible assembly of dimensions within specific eventspaces at even the lowest granularity of raw atomic data, advanced datamining techniques may be employed. These techniques include but are notlimited to cluster analysis, logistic regression, association rulemining, Naïve Bayesian analysis, can be readily applied to such dataarchitectures to generate some valuable findings. The novel methodsdescribed herein allow for a fundamental re-architecture and remodelingof the standard commerce data model to include a specialized fact tableand augmented dimensions to create an advanced high-performance systemfor target marketing and merchandising.

In contrast, the current order-centric star schema data model 80,illustrated in FIG. 7, is an artifact of the brick-and-mortar businessmodel from which it was originally designed to represent and helpoptimize. Given that traditional offline business models only had thecash register as the sole POS transactional system of record forcustomer activity based on orders. The order event became the centralcommerce event of focus. Nearly all commerce metrics were based onaspects of sales and profits due to orders and shipments. As a result,nearly all of the attributes of the customer, as well as the product,were based on sales. For a considerable amount of time,demographic/psychographic and non-purchase transactional data wereignored. Examples of demographic/psychographic data may includeresidence state, zip code, gender, age, and household income. Examplesof non-purchase transactional data may includeRecency-Frequency-Monetary (RFM) scores, customer Long-Term-Value (LTV)scores, and credit card used. In various embodiments of the methodsprovided herein, the use of such attributes and events, whetherexplicitly gathered or statistically derived, increases sales in thee-commerce world.

The e-commerce data model, illustrated in FIG. 10, is a radicaldeparture from the brick-and-mortar paradigm, and yet nearly all retaildata analytic systems do not use a central data model that reflects thisfundamental change. It is far more dynamic in terms of the sheer scaleof transactional activity executed by an exponentially growing consumerbase that is continuously browsing, viewing, cart inserting, abandoning,searching, zooming, price comparing, reading, purchasing, gift wrapping,emailing, and shipping an equally vast array of products.

To meet the need to process and store such unprecedented volumes of richtransactional data, such as, clickstream data, companies invested in ahost of new technologies and systems that collect and store largeramounts of data. Much of this focused on the development of powerful newRelational Data Base Management Systems (RDMS) that could better manageand expediently process such large volumes of data with improved queryresponse times through improved indexing, partitioning, and aggregationstrategies. This also led to the emergence of powerful and highlyintegrated new Decision Support Systems (DSS) software applications thatcombined high performance data warehouse systems (in various OLAPmodalities) with new commerce application servers. This combinationallowed email deployment and campaign management systems to createunified view of the customer across an entire enterprise designed tomeet the needs of a growing tide of customers navigating in a new marketof infinite choices.

Yet despite the advancements in the volume and richness of data relatedthe page-clicking activity of every online browser and the investment intechnologies to mine such data, newer more powerful and nuanced criteriato target customers based on their online activity, have yet to be fullydeveloped and capitalized upon.

Presumably much of this investment in commercial data processing systemsfor the new economy has failed to address a fundamental aspect of thedata itself and how it is assembled. In an area where rapid provision ofcritical information is important, the current order-centric data model,has become a rate limiting step in unlocking the true economicpossibilities of personalization technology. This is due in part to itsfailure as it fails to easily incorporate and expediently processmagnitudes more immensely valuable online transactional data (fact-basedevent data) in addition to order events such as category browsing,product viewing, cart insertion, and searching events. It is a silentculprit that fundamentally precludes the mining and effective use ofinordinately richer sets of data leaving millions in unrealized revenuesevery day.

There are number of reasons why the use of demographic/psychographic andorder-centric customer attributes are severely limited in driving salesin the e-commerce model, there are three factors of particularimportance. The first, often described as a cold-start dilemma is anincreased scarcity of buyers to which such demographic/psychographic andorder-centric attributes can be applied for targeting. The massiveexplosion of online visitors has essentially diluted the use ofotherwise valuable demographic/psychographic and order-centricattributes. Only a very small population of online visitors areidentifiable and have ever made a purchase thus making such attributesincreasingly obsolete as targeting modalities. Thus the vast majority ofcustomers which are unknown will not have such attributes available fortargeting. In addition, the corollary cold-start dilemma also emergeswith regards to products as the only a few products have been purchasedand thus effectively offered to an equally small group of customers thathave actually purchased or for which demographic/psychographic data isavailable.

Secondly, as shown in FIG. 9, for the small population of customers forwhich such attributes are available, they often prove far too general toprovide any truly differentiated product offerings and thus effectivemeans of targeted marketing and merchandising. Further, themulti-resolutional nature of the attributes makes it difficult tonormalize into a single table.

Lastly, when using order data to target customers based on the previouspurchase of a particular product or sets of products, there are inherentlimitations to repeat purchases of these products. While previouspurchase of a particular product has often been an effective predictorof secondary purchase activity, depending on the type of product, thereare natural limitations on the number of lawn mowers, digital cameras,and brown suede belts a given consumer will purchase in his/herlifetime. As a result, repeat order conversions of products quicklyplummet and serve as a very limited source of recurring revenue.

Including more transactional data points can expose new and potentiallybeneficial models which can be used to drive commerce dynamics and thusprofits. This has been particularly true in the case of clickstream dataor weblogs which have been used in recent years to provide insight intoaggregate in-store traffic activity and shopping patterns for onlineretailers. One feature of methods provided herein is that they may useof clickstream data for aggregate analysis of overall shopping activityand traffic and browsing patterns. In the methods provided herein, theuse of clickstream data for aggregate clickpath, traffic, and browsinganalysis, is largely an analytical and overcomes some of the inherentchallenges of incorporating clickstream data into standard databasemodels.

Some of the challenges in working with clickstream data are based onextensive experience in developing advanced enterprise-wide datawarehouse systems for leading e-commerce clients. The complexity of thesystems which were designed in which clickstream data was the centralfocus, offered particular insight into the caveats and power of its usethat is otherwise unknown in other models where such voluminous data islargely ancillary and thus minimally incorporated.

One primary challenge in incorporating clickstream data lies in thefundamental way in which transactional data is commonly assembled andmodeled in databases. As discussed previously, since all actions orevents are represented as the central fact tables in the canonicalstar-schema model, the numerous transactional events that are capturedby clickstream data add a considerable amount of complexity to thestandard model. Taken individually, each of these events (i.e., CategoryBrowse, Search, Zoom, Register, Add to Cart, Order, Remove from Cart,etc.) are typically represented as separate fact tables to allowmultidimensional analysis and modeling. Given the value of providingricher and more expansive insight into specific transactional activitybeyond than the oft focused Order event, it has been a prudent strategyto incorporate such high resolution transactional activity across amyriad of dimensions in efforts to gain better insight.

While the incorporation of clickstream data in a normalized manner assegregated events represented as fact tables is an extension of thestar-schema data model 80, as illustrated in FIG. 11, there aresignificant limitations that become evident when performing criticalrun-time analysis that render the incorporation of such data minimallyuseful, often void, and costly.

FIG. 11 illustrates the incorporation of clickstream data into thestandard star-schema data model 80. It is logically feasible and oftenseamless but its functionality presumes an inherently myopic view of thedata. In essence, facts are traditionally considered individually formultidimensional modeling. This is due in large part to the typicalexistence of only one transactional event of record, the order facttable. When the standard model is further normalized to include othertraditional events and fact tables such as Shipments or Returns, theycan be easily related and analyzed in unison since they are ostensiblyevents all related to an Order being made. As a result, all three facttables can share the same resolution or granularity (often the OrderLine grain) and thus further enabling the sharing of the same conformeddimensions, primarily Customer, Stock Keeping Unit (Sku)/Product, andDate.

To overcome the inherent limitations of purchase history as criteria fortargeting customers, e-commerce companies have tried numerousstrategies. These are typically directed at improving theirunderstanding about the varying interests of their individual customersin efforts to appeal to their tastes in a more dynamic fashion. Thestandard approaches all involve collection of explicit data, usuallyduring the account registration process, from customers about theirindividual interests, preferences, and tastes. In addition to thedetermination of customer interests based on the profile informationthat they provide during account registration and management, theprovision of customer ratings, feedback, and survey data, often used byChoice-Modeling techniques and Collaborative Filtering, can be used todetermine the interests of individual customers as well as infer theinterest of a larger population.

If developed correctly, the schema, can include numerous fact tables canbe combined in creative and powerful ways to generate important newmetrics during run-time, or even more effectively as an attribute of thecustomer. In the case of the standard order-centric model for example,net profitability can be calculated for individual customers in additionto gross sales by joining across the Order, Shipment, and Return facttables with standard formulations involving revenue, shipment, and costof return figures which can be found separately within respective facttables.

The use of multiple events is a powerful logical extension of thecanonical star-schema which is often overlooked. Given the seeminglylimitless amount of multidimensional analysis that can be performedaround a singular fact, multi-event fact table analysis, while immenselypowerful, is often never considered or immensely underutilized becausesignificant increases in the costs of storage and processing time. Whilethe cost of storage has become less expensive over time, processing timeremains a considerable limitation when performing multi-event modelingdue to the significant increases in relational complexity involvingfully conformed dimensions with granularly consistent high volume facttables larger which often undergo expensive full or partial scans andjoins.

Some of the difficulties encountered include difficulties in Joinoperations across facts due to lack of conformed dimensions anddifferent granularities. Additionally, customer dimension may becomeunconformed because massive explosion of anonymous visitors generatingclicks that can no longer be associated with customer dimension.

Even if the volume of the data were more manageable there is stillanother important limitation in performing analysis of clickstream datawhich involves the lack of conformity of the relational dimensions. Byincluding new and unconventional new events, there is the increasedlikelihood that many of them will not share the same dimensions andgranularity because they are entirely distinct and non-related actions.In such cases, many of these events, represented as fact tables, havetheir own distinct dimensions, such as the search event, which has itsown dimension for attributes of the searching activity such as keywordsused. One feature of the present invention is that it may employ asingular fact table where user preferences may be normalized to aparticular resolution or granularity.

The use of individualized dimensions is useful for multidimensionalanalysis of a singular event. When performing analysis across multiplefact tables, the issue of dimensional non-conformity becomes asignificant analytical limitation. For instance, in the case of acategory browsing event, its representative fact table can not berelated to a Sku or Product dimension as with other traditional facttable events such as Orders because the user is not interacting withSkus during this activity. They are instead interacting with products ata higher taxonomy, in particular, categories of products and, as aresult, have a logical relationship with a Category dimension.Furthermore, the Category Browsing fact table would be modeled at aCategory-level product granularity where as others events such as BasketInsert or Order would be modeled at the Sku-level product hierarchy.This non-conformity of dimensions may preclude proper and efficienttraversing and joining across multiple events and hence fact tables thuscreating a barrier to important and otherwise very powerful analyticalmodeling of e-commerce data. One aspect of methods provided herein isthey overcome these inherent limitations of non-conformity.

Additionally, current methods have limited semantical enrichment andentity resolution which warrants equal consideration. Designedoriginally to capture the file request logs of various servers,clickstream data was system-centric as it provided important informationabout aggregate file request usage, a proxy for traffic usage, andoverall insight for the operation and management of the data processingarchitecture by enterprise-wide systems. As a result, there has beenlittle to no visibility into differentiated shopping activities byactual customers in the relation to the universe of available content ormerchandise. This is due in part to page id requests and IP addresseswere the only parameters captured. The use of IP addresses introducedfurther levels of customer abstraction as many were proxy IP addressesassigned by commercial ISPs.

In recent years however, clickstream data has been augmented to includeunique identifiers to key entities such as customers, products, content,campaigns, affiliates, visits, orders, etc. to improve downstreamsemantical resolution to respective attributes found in dimensionaltables for important analytical insight. There are, however, importantlimitations to note in terms of the resolution of customers that canoften lead to blindspots in customer analysis. In particular, the cookieid is used often as a proxy for the individual customers and in the casewhere behaviors and shopping habits of individual customers are beingdetermined based on their clickstream activity, the models that aregenerated are technically that of an anonymous user or often set ofusers that are using a computer. Consider further the occasions whencustomers delete their cookies and hence new IDs get generated and therelational integrity of actual customers is further compromised. Hence,with potential many-to-many relationships between customer ids andcookie ids, the assumption can not always be made that the recordedclickstream activity is actually generated by and thus can be assignedto a specific user.

This limitation may be overcome in some instances where the user decidesto log in, manage their account, or make a purchase, customer resolutioncan be assured as their clickstream activity can be directly associatedwith their customer ID which is implicitly provided in addition to theircookie ID.

All these factors have resulted in the common non-use, underutilization,and misuse of clickstream data in e-commerce analysis and have thuscontributed to the meager performances of current personalizationtechnologies given the inherent reliance on richer data about customerhabits and activities. As a result, clickstream data is typically nolonger collected internally by most e-commerce companies and, if so, isarchived or purged despite the recognition of its potential hidden valuebecause it is considered far too costly, complex, and unwieldy toprovide any significant and consistent economic benefit.

Nonetheless, there is considerable development in new technologies toeffectively mine and capitalize upon the vast torrents of clickstreamdata that continues to be collected for ever increasing numbers ofonline companies and their customers. While there has been some economicsuccess in certain applications of clickstream analytics, many of thesetechnologies fail to unlock the fuller potential of this inordinateamount of online transactional data because they have not transcendedthe traditional singular-event focused data model. Even though popularweb analytic service providers have managed to successfully enrichclickstream data to provide powerful reporting intelligence on aggregatebrowsing behavior and shopping patterns, they have constrained theiranalysis to the most important online actions, either in unison orindependently, without critical dimensional perspective. There is oftenno richer contextual understanding, often represented as a model, ofwhether actions exhibited by such customers necessarily lead tomore/less purchases because that would require, as previously discussed,introducing the immense complexities of traversing multiple clickstreamfact tables. It is therefore an object of the methods provided herein toenable more efficient utilization of clickstream data.

As a result, the spectrum of online actions generated by end-usersduring visits are often considered independently from each other givingonly an aggregate non-contextualized view of what is occurring onlineand with limited insight into why it is occurring and by whom. Even inrare cases where individual events such as Product Views, Searches, andOrders can be tied back to individual users, this does not ensure thecritical capacity to actually target such individuals because their userids generated by the analytic service provider are usually differentfrom the internal keys generated by the client companies thus precludingany data resolution of customers. As such, there remains a major void incurrent web analytic providers to effectively provide the capability forclient companies to target their customers with particular merchandisebased on their browsing behavior because the information provided aboutimportant online transactional events are agnostic to individualcustomers and products.

Given this considerable targeting limitation prevalent in current webanalytic providers, in more advanced clickstream-based personalizationservices are providing real-time in-session marketing and merchandisingoffers. Consistent with various embodiments of the present invention asystem and methods for advanced electronic commerce has been developedwhich enables users to increase order conversion and profits. Themethods provided herein are a paradigm shift from the traditionalcommerce data model because they reflect a fundamental departure fromthe conceptualization of the traditional business models. Onlineshopping, like real world offline shopping, entails a wide myriad oftransactions and events not solely limited to Order transactions. Theorder transaction, as mentioned previously, has been the transactionalevent of focus and data modeling because it is the only action that hasbeen traditionally recorded because of inherent technical limitations ofcommerce systems.

The system and methods provided herein may take advantage oforder-centric commerce models, as illustrated in FIG. 12, in additionwith other clickstream parameters and e-commerce transactional models tocreate more useful analytic systems and methods for e-commerce.

The Internet has revolutionized the commerce landscape and standardbusiness model by not only enabling a more open channel for continuousand highly scalable transactions, it has also provided a new realm oftransactional data of immense dimensional richness and activityresolution to provide unparalleled insight to optimize sales. Whenanalyzed in aggregate, clickstream data can provide some importantinsight into higher-order traffic and shopping patterns but when modeledat the level of individual users and skus its analytical and henceeconomic value can be staggering.

One feature of the methods described herein is the use of clickstreamdata as a window into the shopping psychology, intentions, and interestsof individual or segments of customers based on their interaction withproducts and content or groups of such offerings online. The methodsdescribed herein overcome many of the limitations of the currentcommerce model illustrated in FIG. 12.

In creation of an e-commerce transactional model, other events, whichmay be derived events or may be events present in clickstream data canbe considered as indicators of taste and preferences, then inclusion ofseveral other events, provides manifold enrichment to a customer'sprofile of interest with a wider range of products and classes ofmerchandise.

The transactional expansion provides significant and immediate economiclift by greatly increasing the number of relevant products to present toan individual customer. In context of various embodiments, theseproducts may be recommended by a model that considers parameters beyondthe singular event of what they have already purchased and may includewhat they have demonstrated interest in buying based on what they haveviewed, browsed, inserted into their cart, searched for, etc. Suchindividualized targeting based on a broad and cumulative range ofinterests and thus product offerings serves to dramatically increase acustomer's buying likelihood. Moreover, in addition to influencing thebuying activity of an individual customer that may have been a previouspurchaser, use of this implicit approach to determining interests haseven far greater economic impact as it provides a highly effective meansof targeting the disproportionately larger population of visitors thathave interacted with products in meaningful ways despite having nevermade a purchase. Hence, in the transactional expansion which involvesthe inclusion of clickstream data in modeling individual shoppingactivity can have a compelling economic impact by significantlyincreasing the target population of prospective buyers based on theirindividual interests and behavior with specific products.

Recognizing the intensive data processing limitations due to the everincreasing relational complexity of more dynamic and voluminous data,the methods described herein, a fundamental reconfiguration ofclickstream data is modeled in the database. Thus, instead ofnormalizing the data into separate commerce events and hence separatefact tables, in one embodiment various events may combined into onecentral fact table that become the analytical representation of anoverall behavior. Clickstream data is rich in the types of events thatmay be present, or derived. In one embodiment, a central fact table maycomprise a plurality of event types. Many of these events may be presentin the clickstream. In some embodiments, central fact table events maybe derived event types wherein the data present in the clickstream maybe combined with other factors. In its fundamental form, a behavior canbe considered a series of linked events and in the context of shopping,there are a universal set of distinct events that be combined torepresent various shopping behaviors.

Some of the more universal type of events exhibited by the shopper inrelation to a product are captured in clickstream data and can typicallybe represented as individual fact tables in the analytic data model.But, as has been discussed previously, it is the consideration of thesecustomer actions as separate events and thus separate fact tables thathas been the central limiting factor in high-throughput modeling ofclickstream data as an effective source of personalization and targetmarketing and merchandising.

It is one thing to know what the most popular search terms are andwhether they reflect actual products that are being carried or availablein inventory. It is another thing to know what types of customers areperforming these high frequency searches and whether or not they lead toevaluation of products and eventual purchase of hopefully high marginproducts. The latter form of analysis, as evident, requires the jointmodeling of several events, in particular, the Search, Product View, andOrder events. Any such attempt to model across multiple events spaces(fact tables), particularly clickstream data, becomes immenselyprocessing intensive during run-time and often impossible due toincreased normalization, data volume, and dimensional non-conformity.

As illustrated in FIG. 13, and discussed above, the typical integrationof clickstream activity is in discrete events incorporated into thestandard star-schema data model 80. The sheer size of the these datasets make its utility limited and its run time-processing extremelytime-intensive, if not logically impossible.

In various provided embodiments, creating a singular commerce event thatcan overcome these technical limitations and thereby provide a trulypowerful means of personalization. Significant performance increases arerealized. In most contexts, the actions exhibited by users are notentirely random, as they are often oriented to a particular goal. Thisis no different in the case of shopping and in this particular case,doing so in the grocery store where the goal of the visitor is topurchase a product. While the prevailing action of focus is that of thepurchase, there are a universal series of events, in sequential logic,that eventually lead to the purchase.

As illustrated in FIG. 14, the set of actions exhibited by the shoppercan be largely classified into a standard set of actions occurring in alinear process that often begins with entering the store, trying tolocate a particular product or sets of products either through passivebrowsing or active searching often with the aid of a sales clerk,eventually finding and evaluating the product, considering it forpurchase by placing it into a grocery cart, and eventually making apurchase. Consideration of this entire shopping process is important andcan be applied to every customer for every sku with which they areinteracting as they consider it for purchase.

When examined further, as illustrated in FIG. 15, a more informativedecision-based shopping process emerges where each event becomesconditionally dependent on other events given whether or not the goalsof the shopper are being met along the way. In particular, the types ofevents exhibited and their frequency can provide valuable insight intothe shopping psychology or the individual. For example, while anindividual may vacillate between both modalities, demonstration ofbrowsing activity can indicate a more passive and undirectedconsideration of products whereas requesting the aid of a sales clerk,especially early on during the visit, suggests a more active andsurgical shopping mind frame.

As illustrated in FIG. 16, the same conditional shopping paradigm can beapplied to e-commerce with the added and distinctive advantage thatnearly all events exhibited by the user, can now be recorded in the formof clickstream data and mined for the development of powerful newconstructs provided by various embodiments of methods provided herein.

Whether explicitly recorded as individual events or selectively parsedand partitioned by initial processes of the methods, the file/pagerequest logs of clickstream data can be classified into higher-orderuniversal commerce events. These events can provide insight intodistinctive combinatorial shopping behaviors on the part of individualcustomers. To achieve these individualized shopping behaviors and thusinterests, a number of exhibited actions by the online consumer areconsidered in the methods provided. These actions are considered assignificant events that may be indicative of a shopper's interest andintent in making a purchase. However, there are several limitations thatarise as a result of this new approach the most pronounced being theassociated technical costs of additional storage and processing time tomanage and model exponentially more data points from the inclusion ofthese additional clickstream events.

As illustrated in FIG. 17, to avert the technical complexities ofmodeling these multiple events as distinct fact tables, they may becombined into a singular fact table to represent an emergent behavioralontology of interest and interaction of customers with an entire rangeof products. Exemplary events illustrated in FIG. 17 are product view,cart insert, and purchase.

When considered in unison, these various events ostensibly represent amore expansive and enriched singular event space to signify a customer'svarying levels of interaction and thus interest serving as the basis ofa shopping interaction score for every consumer. Although the customermay have only purchased one product, a dress, she has nonethelessinteracted with a number of other products with various degrees ofinterest and commitment based on the depth of her shopping conversionwith respect to each of them individually.

One feature of the system provided is that based on the level ofenriched transactional intelligence, a Shopping Interaction Score canprovide powerful insight into the preferential interest or affinity ofindividual customers to individual products based on implicitcalculations of their dynamic clickstream data. In one embodiment, ashopping interaction score may be derived from different types ofevents. Other personalization solutions often require that customersexplicitly indicate their preferences and interests or actively rateproducts or answer survey questions in order to improve the accuracy andoverall performance of their analytics. Here, the described modelrequires no such input from customers. Instead, the interests andpreferences of customers are passively derived based on their implicitand unbiased activity online as they interact with products.

From a targeting and personalization standpoint, customers can now beselectively targeted with a larger and more pertinent choice set ofproducts based on their differential shopping behavior. Furthermore,products can be ranked to enable preferential offering based on thecustomer's Shopping Interaction Score. As such, while the customer mayhave already bought a handbag twice and a dress, we may chose to offerher, based on her interaction and inferred interest profile, the pair ofshoes and, at some later date, the green blouse and lastly the red one.

If we were to consider two customers that purchase the same product, thehandbag, ordinarily, based on standard order-centric analyticapproaches, there would be no way of differentiating the interests ofthese two customers to the product. Perhaps if multiple orders had beenmade, for instance Customer A having purchased 3 bags versus customer Bthat only bought one, assumptions could be made that Customer A has agreater “interest” or “affinity” to PRADA handbags than Customer B.However, sole reliance on multiple purchasing activity precludes furtherinsight into compelling differences in their buying behavior.

Based on the construction of a Shopping Interaction Model, variousembodiments include the previous events exhibited prior to the purchaseof the product, and exploit the significant differences in the shoppingbehavior between the two customers. Based on the relative frequency ofevents exhibited across a frequency of visits (sessions), new shoppingbehavioral patterns emerge. Further, Customer A is far more stringentand exhaustive in evaluating the handbag prior to purchasing it, havingexhibited a specific set of behaviors over a greater number of visits,and thus far less of an efficient shopper than Customer B. Based onaggregated frequency distributions of these shopping interaction events,new insight can be provided into various shopping behavioral ontologiesexhibited by individual customers or segments of customers in relationto specific products or sets of products at taxonomical levels.

One benefit of this ontological model is that it enables powerfulbehavioral micro-segmentation and targeting of customers in order tomore effectively influence their purchasing activity. When consideringthe overall shopping process, there are natural friction-points,illustrated in FIG. 18, such as price, breadth of selection,communicated value, and visibility of product, which preclude completeand efficient purchase activity. This may be particularly true whenconsidering the impeding factors that may cause a customer to abandonand completely forgo purchasing a product after viewing it in somedetail or even inserting it into their shopping cart. In particular, itis the aggressive price point, inadequate selection, and/or low valueproposition of products that all serve, in various degrees, assignificant barriers to purchase and they can essentially be inferredand selectively pin-pointed and addressed by these new shoppingontologies that are derived.

Thus, the frequency distribution of these conditional events allinvolved in the purchase of a product assimilated into a novel attributethat represents a customers level of interaction as well as interestoffers unparalleled targeting capabilities. One feature of variousembodiments is that not only does the model provide a business user withinformation of what customers are interested in which products (and viceversa) but also insight into some of the reasons why they have yet tomake a purchase despite their interest. Certainly an understanding ofthese impeding factors may be used to inform the type of marketing andmerchandising treatment offered to specific customers such as selectivediscounts or additional information on products or alerts on when newproducts or related products have arrived or special sales events orclearances. It is likely that far too many price-insensitive shoppersare arbitrarily offered discounts all of the time on products that theywould otherwise pay for at original prices because there remains theoverwhelming perception that this may be the best way to minimizeinventory risk and ensure a purchase. Moreover, companies often have noway of accurately determining the relative price sensitivity of theircustomers and thus have no the means of micro-segmenting them based onthis dimension.

Thus in the wake of indiscriminate treatments such as generalized pricereductions, the system and methods provided herein and illustrated inFIG. 19, allow for an alternate consideration that purchasing activitycan be equally influenced, with the selective offering of the rightproduct(s) to the right customer(s) based on the customers' observedinterests. As such, a customer may just as likely to buy a sweater froman email alert that they receive about the arrival of such a sweater intheir size and their favorite brand than an email about 10%-off on allsweaters. Further, based on the use of the system and methods,purchasing activity could be further influenced by the selectiveinterest-based targeting of customers that may have only browsed lightlyor seriously evaluated a product and considered it for purchase only toretract that decision at the last minute of their shopping visit insteadof those who may have already purchased it and are thus perhaps lesslikely to do so again for a third or fourth time.

Thus, whether there may be price-sensitivity or inadequate selection ofproduct based on the various dimensions of the product (i.e., size,color, category, brand, etc.), the system and methods can providepowerful insight into often hidden shopping dynamics to enablehigh-performance precision marketing and merchandising at any preferredresolution (dimensional aggregation) involving customers and products. Anumber of events and other parameters may be modeled, as illustrated inFIG. 20.

These are some of the emergent dynamics that serve as the foundation forthis high-performance personalization system that can be applied acrossa wide range of enterprise systems to optimize commerce and drive saleswith unparalleled economic lift. One advantage of this advanced systemlies with the high-throughput modeling of clickstream data based on afundamental redesign of the commerce data model to a more denormalizedintelligent fact table to reflect a more expansive and thus effectivebehavioral model.

Systems and methods provided herein provide for a new e-commerce modelthat allows the modeling of customer behavior to determine interest andforecast purchasing behavior. These systems and methods may utilizeimplicit behavioral attributes, known as customer-product attributes toprovide two major advantages. They allow the online merchant to includeproducts and preferences in customer profiles and they expand the “eventspace” to include differentiated interests.

In one embodiment, illustrated in FIG. 21, a system, is provided toproduce high-performance campaign models for companies based on theiruniverse of customers and merchandise. As such, the data about thesemajor entities (Customers 10 and Products 20) as well as thetransactional data between them is collected continuously from variousdata sources. In this embodiment, business user 130 interacts withcomputing device 120 and configures the analytic engine present oncomputing device 120 a to generate a target marketing campaign throughcomputing device 120 b containing an email engine. In this embodiment,analytic data base 140 receives data from campaign database 110, datawarehouse 100, and clickstream database 90. As illustrated, someembodiments may include an OLTP database 50 which sends data to datawarehouse database 100, and interacts with computing devices 120 c,illustrated as application servers. Computing devices 120 c interactwith customer 10, through computing device 120 d displaying aninteractive Graphical User Interface (GUI) such as a website.

Through the configuration of computing apparatuses 120 a-120 c, atargeted email campaign can be generated and sent to a select group ofpotential customers in a target population.

Three major sources have been identified as common sources of thevaluable data of customer's page clicking activity or clickstream datastored in clickstream database 90, on a client's particular site. Thereare distinct advantages and disadvantages to these various data sourcesand much of this determination is based on the availability andintegrity of the initial set of data points. As the present inventionmay employ a number of different sources of activity data, the presentinvention is not limited with respect to data source.

As illustrated in FIG. 22, various formats, most popularly as webserverlog files, clickstream data often has a vast set of parameters that canbe captured. As further illustrated, clickstream database 90 maycomprise a multiplicity of clickstream databases 90 including onepresent on a business client's system and clickstream databases 90 bwhich are external sources of clickstream data. As illustrated, customerpage browsing data can be collected directly from companies whosecomputing devices 120 c (application servers) may be configured tocollect web log data. The metadata of these log files have become fairlystandardized over the past few years and many commerce applicationservers (i.e., ATG, Blue Martini, etc.) have more enriched data points.More importantly, the meta data of the clickstream data file areconfigurable that eventually allow the collection of rich data such asexternal tracking codes, such as promotional codes, marketing codes,campaign codes, coupon codes, and affiliate codes.

Using clickstream data to derive models in this manner has a number ofadvantages. First, clickstream data that is collected by a company'sinternal data systems is relatively easy to semantic enrich. Given thatthis data will be coming directly from a company's database systems,semantic resolution, or mapping of these transactional files tocustomer, product, and other dimensional data, is much simpler becauseof the availability of such dimensional tables within the enterprise. Inparticular, the system may have access to critical data about customersand products, especially the dynamic properties such as the email opt-instatus, inventory position, pricing and assortment position, etc.

A second advantage is the relative ease of gathering the data thusmaking it a non-intrusive passive form of integration. Often such datais collected in databases that are ancillary to core business systemssuch a production application databases or data warehouses only to betemporarily stored, very rarely used, and often purged or archived.

As is known in the art, several sources, described as site analyticsources, provide reporting services to online businesses by collectingtheir own set of clickstream data based on the activities of a company'svisitors on their individual websites. In some embodiments direct enduser browser feed may be a source of clickstream data. A number ofadvantages exist in using third-party clickstream data. As stated above,since a number of sources of clickstream data are readily available, andmay practice the current invention, the invention is not limited withrespect to the data source.

In other embodiments, Order Data may be additionally incorporated. Whileorder, or purchase event, data can usually be collected from clickstreamlogs, for more stringent auditing resolution of sales metrics (volume,price, promotion codes, shipping costs, campaign codes, coupon codes,etc.) standard order-based transactional data should also be collectedto enrich the methods. In particular, these keys may be present toincorporate such back-end order data with the clickstream data: CustomerID; Cookie ID; Order ID; and Session ID. In some embodiments, this typeof “back-end” data may be collected from the business client's OLTPdatabase systems or OLAP data warehouse/data mart database systems.

The systems and methods provided, may utilize a set of customer IDsgenerated and managed by the internal systems of the business client viaautomated incremental loads. While it is not necessary to have access tothe other attributes of customers internally derived by business clients(i.e., contact data, demographic/psychographic data, transactional data,etc.), the ability to incorporate such dimensional data may increase thebusiness user's ability to do more high-performance customersegmentation and targeting. Customer data can be collected from thebusiness client's OLTP database systems or, preferably, OLAP datawarehouse/data mart database systems.

The systems and methods described herein may utilize a master set ofmerchandise-related and/or content-related IDs along with concomitanttaxonomies/hierarchies generated and managed by the internal systems ofthe business client. Access to nearly all attributes/dimensions ofmerchandise, many of which are dynamic such as price and inventoryavailability, provides critical data for the multivariate(multidimensional) architecture of the methods and its resultant models.This product data can be collected from the business client's OLTPdatabase systems, inventory management systems, or OLAP datawarehouse/data mart database systems.

While the importance of marketing and merchandizing stimuli can not beoverstated, there are significant limitations in its capture andanalysis. Rarely is the rich semantic data (items featured, text used,prices offered, number of target recipients, etc.) about campaigns andpromotions that are created by business users captured and maintained incampaign management engines due to the limited metadata framework. Thisis even more the case in terms of dynamic campaigns that are often usedfor highly targeted emails. Often the rich and detailed metadata ofthese individual in-page treatments that are dynamically presented oftenbased on a personalization rules engine, as in the case of advancedtargeted emails, are not captured and thus analyzed.

Additional data sources may include both internal and external systems.Campaign and promotion data can be captured in limited modalities fromthe business client's have campaign management systems that are oftenstandard packaged modules of various commerce server applications.Additionally, given the more standard use of third-party email deliveryservice providers, the metadata of campaign designs as well asperformance metrics can be collected from their external systems uponrequest.

The system may be configured to provide high-performance advertising ormarketing campaign models for companies based on their universe ofcustomers and merchandise. As such the data about these major entities,Customers and Products, as well as the transactional data areaggregated.

One embodiment of a method is illustrated in FIG. 23. In block 150source data is collected from a plurality of sources, described above.In block 160 the source data is aggregated into a base affinity schemathat calculates the affinities of consumers to various entities (i.e.,products, content, service, etc.) across various multidimensionalcombinations such as implicit taxanomic relations. By way of example andnot limitation, the source data may include clickstream data, back-endorder data, source product data, and source customer data. As describedabove, there a number of sources of this type of data available.Additionally, other types of data may be utilized. The Base AffinitySchema may include a Product Attribute Map yielding a product entity; aSku Attribute map yielding a Sku Entity, a Customer Attribute Mapyielding a Customer entity; a data. An Interaction Table may be derivedfrom these and other Entities, as described above. Flow continues tobehavioral micro-segmentation block 170, where multivariate customerscores and Materialized n-Dimensional (Aggregate) Scores, and Run timen-Dimensional (Aggregate) Scores are derived. It is important to notethat the method illustrated in FIG. 23 combines a pre-processing andrun-time blocks. One skilled in the art will realize that these blocksmay execute at different times.

As shown, the Materialized n-Dimensional Aggregate Scores may comprisevarious aspects of customer-product interaction based on variouscombinations of a breadth of distinct events (actions), the frequencywith which such event combinations are exhibited, including temporalfactors. Such variables serving as components a shopping interactionscore are parameters which may be used to calculate an n-dimensionalCustomer-Product Buying Probability.

An alternate embodiment is illustrated in FIG. 34. This embodiment issimilar to the one illustrated in FIG. 23 with a few distinctions. Inthis embodiment, the aggregate set of affinity scores are included in an-dimensional an aggregate customer-product interaction event-typeclassification, a n-dimensional aggregate customer-product interactionrecency classification, and a n-dimensional aggregate customer-productinteraction frequency classification. Further, in some embodiments, asillustrated, the run-time set of affinity scores include a run-timen-dimensional customer-product interaction recency classification, and an-dimensional run-time customer-product interaction frequencyclassification. In some embodiments, the run-time affinity scores arederived, or extrapolated, from the aggregate scores.

A buying probability may be calculated based on any number of knownmethods including a Hierarchical Bayesian calculation. As is known inthe art, Hierarchical Bayesian techniques include analysis and decisionmaking methods which may be based on semi-subjective probabilities (orinferences) coupled with uncertainties (or likelihoods) of eventoccurrence. Bayesian inference uses a numerical estimate of the degreeof belief in a hypothesis before evidence has been observed andcalculates a numerical estimate of the degree of belief in thehypothesis after evidence has been observed. Bayesian inference usuallyrelies on degrees of belief, or subjective probabilities, in theinduction process and does not necessarily claim to provide an objectivemethod of induction. Nonetheless, some Bayesian statisticians believeprobabilities can have an objective value and therefore Bayesianinference can provide an objective method of induction.

A particular customer or segments of customer's probability of purchasefor various products is based on the parameters of their derivedinteraction scores. These probabilities may be updated across a numberof discrete time events, to model the interaction and probability forcurrent time which may be used in real-time to target the customer.

An exemplary embodiment of the derivation of a multi-resolutional,multi-dimensional, de-normalized interaction table and affinity schemais further illustrated in FIG. 24. In this embodiment, a clickstreamdata source 190 provides information on a plurality of commerce events.As discussed above, there may be additional data sources employed thatare not illustrated here for convenience. Exemplary commerce eventinformation includes, but are not limited to product view data, cartinsert data, and order data. As illustrated order data may additionallybe received from non-clickstream sources. The commerce events arepartitioned in block 200. In this exemplary embodiment, in block 210 afrequency calculation of commerce events is conducted to produceaggregate information. From this aggregate data, a singular meta eventis derived in block 300 and stored in the multi-dimensional,multi-resolution, de-normalized interaction table 220.

Additionally, as illustrated, product source data 230 which may comprisea product model 240 and a SKU model 250 at different resolutions. Asillustrated the product model includes a product attribute map and aproduct entity, but some embodiments of the invention are not limited tothis exemplary model. In some embodiments the product attribute mapcomprises an attribute identification, and an attribute name and theproduct entity comprises a product identification, an attributeidentification and an attribute value. In like manner, Sku model 250 isillustrated with a SKU attribute map and a SKU entity, for exemplarypurposes. Further inputs to interaction table 220 include sourcecustomer data 260 which may comprise source model 280 exemplified toinclude a customer attribute map and a customer entity. In someembodiments, the customer attribute map comprises an attributeidentification and a attribute name, and the customer entity comprises acustomer identification an attribute identification, and an attributevalue. Interaction table 220 may further include a date 290

Referring to FIG. 25, which illustrates an exemplary embodiment of thecalculation of affinity scores from interaction table 220. As previouslydiscussed interaction table 220 comprises product model 240, SKU model250, customer model 260, and in some embodiments, date 290. It isimportant to note these models are multi-dimensional and de-normalized.Further, since the events span multiple time frames and resolutions,interaction table 220 allows configurable modeling time frames. In block310 a selection of dimensional parameters is performed. In block 320 thecalculation of an interaction score is performed. As illustrated, thecalculation interaction score calculation, in some embodiments comprisescalculating an n-dimensional frequency interaction score, normalizationof the n-dimensional frequency score, and the derivation of ann-dimensional interaction score. Which, in block 330 is used tocalculate a shopping behavioral class. Returning to interaction table220, in block 340 an n-dimensional recency score is calculated and inblock 350 an n-dimensional recent event classification is calculated.

FIGS. 26 and 27 are a further illustration of an exemplary calculationof buying probabilities based on product-centric (FIG. 26) andcustomer-centric (FIG. 27) buying probabilities. In these calculationsan interest score is derived for the product-customer interaction. Inthe product-centric model a population of potential customers isidentified by interest for a particular product, an interest scorecalculates, a buying probability calculated and projected revenuescalculated for the product. In the customer-centric model a customerspreference for products may be modeled. For each product an interestscore may be calculated, a probability of purchase calculated andprojected revenues forecasted and an e-commerce report may be generatedfrom these forecasts.

In one embodiment, illustrated in FIG. 28, an e-commerce report such ascustomer target lists may be generated based on product ranking, derivedinterest level. From these parameters advertising and marketing listsmay be generated that target a customer with the highest probability ofpurchase with their item of most demonstrable interest.

One method provided includes the steps for implicitly scoring andclassifying the interests and preferences of consumers in relation tovarious dimensions of items being offered (i.e., products, content,service packages, etc.) as function of time and utilizing such scores topredict purchasing activity and forecast revenue yield for n-dimensionalcombinations of interest for optimal generation of consumer lists fortarget marketing and merchandising. The method also includes conversemodeling of the performance and behavioral profile of items offered as afunction of consumer activity.

Source data has, for the purposes of this system, been classified intotwo categories: transactional data and dimensional data. Thetransactional data pertains to data generated by specific set of actionsexhibited by a consumer within a specific context. In the case of theonline store, transactional data pertains to actions exhibited by theconsumer in relation to various items which would include, but notlimited to, viewing, reading, searching, purchasing, or downloading. Asis known in the art, there are various data base systems that attempt tostore and process this data to yield value to the user.

In one embodiment of a provided system, illustrated in FIG. 29, datacollection module 360 comprises a transactional data collection moduleand a dimensional data collection module. The two classes of data may bemanaged differently as they are often provided by different sourcesystems at varying incremental update frequencies. The transactionaldata in some embodiments, may be automatically loaded and staged at apredetermined schedule by a designated module for downstream dimensionalresolution and score assembly. In this embodiment, transactional data issent to predictive transaction module 380 which, in some embodimentsincludes a behavioral training module, a behavioral testing module, anda revenue forecasting module. The transactional and dimensional data arefurther sent to dynamic schema management module 390.

Referential dimensional data of all entities involved in varioustransactional events (i.e., consumers, items, campaigns, dates, etc.)are also collected from various source systems and potentiallytransformed, like the transactional data described in the prior module,into schema management module 390. Dynamic schema management module 390manages heterogeneous input data from varying client sources withdifferent metadata and relational structures (i.e., producthierarchies/taxonomies, graphical models, ontologies, etc.) into astandard data model for selective processing. This standardizedmeta-construct allows for efficient multivariate processing to generatebehavioral scores and buying probabilities for optimal customer listsgeneration for targeting per client user. This module appropriatelyselects the requisite data points from the source data as inputvariables for downstream calculations of behavioral scores and buyingprobabilities. This module is also designed to adaptively includeadditional transactional events as well as potentially new relationaldimensions associated with them.

The Multidimensional behavioral scoring module 410 receives inputs frompredictive transaction module 380, dynamic schema management module 390,and in some embodiments, model performance measurement analysis module400. In an exemplary embodiment, it processes scores indicative ofbehavior for selected dimensions in two modalities: pre-compiled(materialized) processing or run-time processing. The calculation streamfor each score processing modality is nearly equivalent, in someinstances, except in the case of the pre-compiled processing whereselect dimensional combinations have been predetermined for scoringeither by client specified rules or implicitly determined by thedescribed system based on usage statistics.

The illustrated embodiment further includes an ad-hoc input module 370which allows a business user to configure or manipulate systemparameters through business user application interface 480 which may bea graphical user interface, such as a webpage or other interface.

The behavioral scores are generated by module 410 based on parametricuser input of consumer, item, and time-based dimensions existent in theavailable data set via the business user application interface module480. Such behavioral scores of consumers are calculated over specifiedtime ranges in order to determine the optimal population of prospectsthat should be targeted with items or groups of items based on predictedbuying probabilities and revenue yield. Such prospects are saved aslists of consumers with unique identifiers for particular item offeringsand made available for targeted marketing and merchandising campaignsacross various channels including email, direct mail, website, mobile,etc.

Targeting lists can be generated by two prevalent user paths. The firstis a consumer-centric path, whereby the user performs a parametricselection of a specific customer segment and the system determines whichitems within the dataset for which there is most interest and associatedlikelihood of purchases and revenue yield. The user can specify theparticular classes or categories of items for which they would like tofind the interest of the consumer segment under consideration fortargeting.

Conversely, the user can employ the other path for target listgeneration by first conducting a performing a parametric selection of aspecific item or group of items and then determining the segment ofconsumers that would be most interested.

Once all scores have been calculated for a set of consumers for aspecified time frame for a particular item or class of items, buyinglikelihoods are calculated for determinations of revenue yields. Suchbuying probabilities are generated by scoring a customer set in a timeframe previous to the initial current period with the same set ofparameters (training period) and then observing their buying behaviorwithin the same historic time frame. These observations of differentialbuying activity represent the full set of joint distributions in theunderlying Hierarchical Bayesian model used to calculate the posteriorprobabilities of purchasing behavior of specific set of customers foritems.

Numerous trials may be performed including a test set in order todetermine the predictive accuracy and reliability of the derived buyingprobabilities to assign to the current set of consumers which has beenscored. Once the buying probabilities have been assigned, calculationsare performed to determine the population of buyers and the concomitantrevenue yield as a function of the item price.

In order to increase processing response time and list generation,behavioral score, buying probabilities, and revenue yield calculationsare pre-compiled in an n-dimensional hypercube comprised of consumer,item, and temporal aggregate combinations. Such materialized aggregatescan be generated for a select set of dimensions either predetermined byuser specifications or dynamically by the system based on usagestatistics.

In addition to allowing users to generate consumer and item lists fortargeted marketing and merchandising based on multivariate determinationof dimensions in their data set, an ad-hoc parametric input module isavailable to allow users to provide custom dimensional combinations(meta-dimensions) as criteria for analysis, segmentation, and targeting.Such dimensions are based on complex business logic and aggregates ofalready existent dimensions and can be calculated either in run-time orprior via materialized result sets.

This specialized user input module illustrated within module 370,increases the targeting capacity and revenue lift performance of thesystem as it allows for heuristic augmentation and information gain withthe inclusion of user-determined dimensional inputs.

Once the user selects the customer list to be generated, the consumerswithin the list as well as the associated metadata of both the targetand treatment components is managed by the customer list managementmodule 460. Lists and their metadata can be either saved by the user forfuture use or exported to external client systems for immediate use. Ifthe list is saved, both the list of customers and the metadata of thelist regarding the item treatment can be reused to generate new variantcampaigns. Both versions of reuse is time sensitive yielding potentiallydifferent list membership profile, population size, as well as differentarray of items to be offered.

When lists are made available for export to external systems specifiedby the user, they are delivered by the customer list deployment module470. In cases where multiple consumer lists are deployed within asimilar time frame, a list membership duplication resolution process isconducted in order to minimize unwanted communication saturation totarget consumers. Unlike other technologies which may perform removal ofduplicates based on arbitrary de-selection criteria, the describedsystem performs optimal list membership assignment based on interestscores and buying probabilities calculated by the system.

In addition to scoring of consumer interests to particular items basedon point-in-time user parametric modeling, the system also performsheuristics on historical behavioral trends. In the case of the customerintelligence module 440, new scores and indexes are calculated thatmeasure the rate and acceleration of particular behavioral nuances ofconsumers in relation to products.

A converse set of behavioral trend scores are also generated in theproduct intelligence module 450 for various item classes in order toidentify emerging trends for improved targeted merchandising to amenableconsumer segments.

Intelligence from both modules can be summarized into a separatereporting service that can provide insight into behavior-driven,micro-markets for various domains.

Additionally, correlations between behavioral trend scores of alldimensions being modeled (i.e., consumers, items, etc.) serve as thepremise for the generation for rule set for the target campaignrecommendation module. Such a module allows for undirected analysis anddiscovery of candidates for high-performing lists for target marketingand merchandising campaigns. The criteria for recommendation by thismodule can be driven by user stringency thresholds or manageddynamically by heuristics of projected and actual list performancemetrics.

Designed as a closed-loop heuristic system for continuous and adaptivelearning, the model performance analysis module 400 provides metrics forthe comparative lift performance analysis for the user. In addition, themodule actively analyzes the performance of models which have beengenerated and utilized for live campaigns in order to refine theselection and use of various input variables and drive the discovery ofnew candidate variables to improve performance.

One embodiment of a provided method is illustrated in FIG. 31. In thisembodiment, flow begins in block 560 where a multi-dimensional,multi-resolutional, de-normalized interaction table is derived. Flowcontinues to block 570 where the interaction table is populated with aplurality of information. Exemplary information includes information onproducts at varying resolutions, information on potential customers, andinformation on events at various times. Flow continues to block 580where predictive models are derived. Flow continues to block 590 wherean e-commerce report is derived from these models.

Another exemplary embodiment of a method is illustrated in FIG. 32. Inthis method, flow begins at block 600 where a set of affinity scores aremodeled from a set of information. The information including informationon products at various resolutions, information on potential customers,and information on events at different times. Flow continues to block610 where buying probabilities are calculated. Flow then continues toblock 620 where an e-commerce report is produced.

An embodiment of another provided method is illustrated in FIG. 33. Inthis embodiment, flow begins in block 630 where a user interface isprovided, the user interface configured to allow a user to interact insuch a way to customize e-commerce reports. Flow continues to block 640where a model of an aggregate set of affinity scores is provided. Flowthen continues to block 650 where a set of run-time affinity scores aregenerated. In some embodiments, the run time set of affinity scores arederived, or extrapolated from, the aggregate set. Flow then continues toblock 660 where a buying probability is calculated from the run time setof affinity scores. Flow continues to block 670 where an e-commercereport is generated. In this, as in other described embodiments, thee-commerce report may include but is not limited to an advertisingcampaign, a revenue forecast report, an inventory predication report, asupply chain report, a product pricing report, a product demand report,a customer-centric product affinity report, and a product-centricproduct infinity report.

Embodiments of provided computing apparatus 120 and computer softwareproduct 550 are illustrated in FIG. 30. Computing device 120 includesprocessor 500, memory 510, storage media 520, input device 530, andmonitor 560. As is known in the art additional components are necessaryto make computing devices functional or additionally useful. Theseadditional components are omitted for convenience. In variousembodiment, media 520 contains a set of processor executableinstructions that, when executed by processor 500 configure computingdevice 120 to execute the methods herein described to produce e-commercereports. In some embodiments, computing device 120 may be connected tonetwork 540 and communicating with other computing devices so connected.A embodiment of computer software product 550 is further illustrated inFIG. 30. Computer software product comprises a computer readable mediaembedded with a set of processor executable instructions that, whenexecuted by processor 500 configure computing device 120 to execute themethods herein described and produce e-commerce reports. In an alternateembodiment, the machine readable media may be located in anothercomputing device 120 across network 540. In this embodiment processorexecutable instructions may be stored on database 550. These processorexecutable instructions sufficient, when executed by processor 500, toconfigure computing device 120 to execute the methods described hereinand generate e-commerce reports.

Thus, it is seen that a system and methods for analytical processing anddetermining customer interests are provided. One skilled in the art willappreciate that the present invention can be practiced by other than theabove-described embodiments, which are presented in this description forpurposes of illustration and not of limitation. The specification anddrawings are not intended to limit the exclusionary scope of this patentdocument. It is noted that various equivalents for the particularembodiments discussed in this description may practice the invention aswell. That is, while the present invention has been described inconjunction with specific embodiments, it is evident that manyalternatives, modifications, permutations and variations will becomeapparent to those of ordinary skill in the art in light of the foregoingdescription. Accordingly, it is intended that the present inventionembrace all such alternatives, modifications and variations as fallwithin the scope of the appended claims. The fact that a product,process or method exhibits differences from one or more of theabove-described exemplary embodiments does not mean that the product orprocess is outside the scope (literal scope and/or otherlegally-recognized scope) of the following claims.

1. A method of producing an e-commerce report comprising providing auser interface, the user interface configured to allow a user tocustomize an e-commerce report; providing a model in the form of anaggregate set of at least one affinity score from a plurality ofinformation, the plurality of information comprising informationselected from a group consisting of information on products at varyingresolutions, information on potential customers, and information onevents at various times; generating a run-time set of affinity scores byextrapolation from at least one of the aggregate set; calculating abuying probability from at least one of the run-time set of affinityscores; and producing an e-commerce report from the buying probability.2. The method of claim 1, wherein the e-commerce report comprises areport selected from a group consisting of: an advertising campaign, arevenue forecast report, an inventory predication report, a supply chainreport, a product pricing report, a product demand report, acustomer-centric product affinity report, and a product-centric productinfinity report.
 3. The method of claim 1, wherein the providing of themodel, the calculation of the buying probability, and the production ofthe e-commerce report are accomplished by an analytical processingsystem, the system comprising: a data collection module; a predictivetransaction module; a schema management module; a behavioral scoringmodule; a customer and product intelligence module; a recommendationengine; and a report generation module.
 4. The method of claim 3,wherein the behavioral scoring module and customer and productintelligence modules generate the aggregate set of at least one affinityscore by: generating a customer-product interaction score; generating acustomer-product recency score; and generating a customer-product recentevent classification.
 5. The method of claim 3, wherein the behavioralscoring module and the customer and product intelligence modulesgenerate the aggregate set of at least one affinity score by: generatingan aggregate customer-product interaction event-type classification;generating an aggregate customer-product interaction recencyclassification; and generating an aggregate customer-product interactionfrequency classification.
 6. The method of claim 3, wherein thebehavioral scoring module and customer and product intelligence modulesgenerate a run-time set of affinity scores for a plurality of customers.7. The method of claim 6, wherein the generation of a run-time set ofaffinity scores comprises: generating a customer-product interactionscore for at least one of the plurality of customers and at least one ofa plurality of products; generating a customer-product recency score forthe at least one of the plurality of customers and the at least one of aplurality of products; and generating a customer-product recent eventclassification for the at least one of the plurality of customers andthe at least one of the products.
 8. The method of claim 6 wherein theaggregate set of affinity scores and the run-time set of affinity scorescomprise scores selected from a group consisting of product-centricscores that indicate a plurality of customer's affinity for a product,and customer-centric affinity scores that indicate customer's affinityfor a plurality of products.
 9. A computing apparatus comprising: aprocessor; a memory communicating with the processor; and a storagemedium, the storage medium comprising a set of processor executableinstructions that, when executed by the processor configure thecomputing apparatus to: provide a user interface, the user interfaceconfigured to allow a user to customize an e-commerce report; provide amodel in the form of an aggregate set of at least one affinity scorefrom a plurality of information, the plurality of information comprisinginformation selected from a group consisting of information on productsat varying resolutions, information on potential customers, andinformation on events at various times; generate a run-time set ofaffinity scores by extrapolation from at least one of the aggregate set;calculating a buying probability from at least one of the run-time setof affinity scores; and produce an e-commerce report from the buyingprobability.
 10. The computing apparatus of claim 9, wherein thee-commerce report comprises a report selected from a group consistingof: an advertising campaign, a revenue forecast report, an inventorypredication report, a supply chain report, and a product pricing report,a product demand report, a customer-centric product affinity report, anda product-centric product infinity report.
 11. The computing apparatusof claim 9, wherein the configuration for providing the model, thecalculation of the buying probability, and the production of thee-commerce report are accomplished by an analytical processing system,the system comprising: a data collection module; a predictivetransaction module; a schema management module; a behavioral scoringmodule; a customer and product intelligence module; a recommendationengine; and a report generation module.
 12. The computing apparatus ofclaim 11, wherein the behavioral scoring module and customer and productintelligence modules generate the aggregate set of at least one affinityscore by: generating a customer-product interaction score; generating acustomer-product recency score; and generating a customer-product recentevent classification.
 13. The computing apparatus of claim 11, whereinthe behavioral scoring module and the customer and product intelligencemodules generate the aggregate set of at least one affinity score by:generating an aggregate customer-product interaction event-typeclassification; generating an aggregate customer-product interactionrecency classification; and generating an aggregate customer-productinteraction frequency classification.
 14. The computing apparatus ofclaim 11, wherein the behavioral scoring module and customer and productintelligence modules generate a run-time set of affinity scores for aplurality of customers.
 15. The computing apparatus of claim 14, whereinthe configuration to generate a run-time set of affinity scorescomprises a configuration to: generate a customer-product interactionscore for at least one of the plurality of customers and at least one ofa plurality of products; generate a customer-product recency score forthe at least one of the plurality of customers and the at least one of aplurality of products; and generate a customer-product recent eventclassification for the at least one of the plurality of customers andthe at least one of the products.
 16. The computing apparatus of claim11, wherein the aggregate set of affinity scores and the run-time set ofaffinity scores comprise scores selected from a group consisting ofproduct-centric scores that indicate a plurality of customer's affinityfor a product, and customer-centric affinity scores that indicatecustomer's affinity for a plurality of products.
 17. A computer softwareproduct comprising: a storage medium comprising a set of processorexecutable instructions that, when executed by a processor configure acomputing apparatus to: provide a user interface, the user interfaceconfigured to allow a user to customize an e-commerce report; provide amodel in the form of an aggregate set of at least one affinity scorefrom a plurality of information, the plurality of information comprisinginformation selected from a group consisting of information on productsat varying resolutions, information on potential customers, andinformation on events at various times; generate a run-time set ofaffinity scores by extrapolation from at least one the aggregate set;calculating a buying probability from at least one of the run-time setof affinity scores; and produce an e-commerce report from the buyingprobability.
 18. The computer software product of claim 17, wherein thee-commerce report comprises a report selected from a group consistingof: an advertising campaign, a revenue forecast report, an inventorypredication report, a supply chain report, and a product pricing report,a product demand report, a customer-centric product affinity report, anda product-centric product infinity report.
 19. The computer softwareproduct of claim 17, wherein the configuration for providing the model,the calculation of the buying probability, and the production of thee-commerce report are accomplished by an analytical processing system,the system comprising: a data collection module; a predictivetransaction module; a schema management module; a behavioral scoringmodule; a customer and product intelligence module; a recommendationengine; and a report generation module.
 20. The computer softwareproduct of claim 19, wherein the behavioral scoring module and customerand product intelligence modules generate the aggregate set of at leastone affinity score by: generating a customer-product interaction score;generating a customer-product recency score; and generating acustomer-product recent event classification.
 21. The computer softwareproduct of claim 19, wherein the behavioral scoring module and thecustomer and product intelligence modules generate the aggregate set ofat least one affinity score by: generating an aggregate customer-productinteraction event-type classification; generating an aggregatecustomer-product interaction recency classification; and generating anaggregate customer-product interaction frequency classification.
 22. Thecomputer software product of claim 19, wherein the behavioral scoringmodule and customer and product intelligence modules generate a run-timeset of affinity scores for a plurality of customers.
 23. The computersoftware product of claim 22, wherein the configuration to generate arun-time set of affinity scores comprises a configuration to: generate acustomer-product interaction score for at least one of the plurality ofcustomers and at least one of a plurality of products; generate acustomer-product recency score for the at least one of the plurality ofcustomers and the at least one of a plurality of products; and generatea customer-product recent event classification for the at least one ofthe plurality of customers and the at least one of the products.
 24. Thecomputer software product of claim 19, wherein the aggregate set ofaffinity scores and the run-time set of affinity scores comprise scoresselected from a group consisting of product-centric scores that indicatea plurality of customer's affinity for a product, and customer-centricaffinity scores that indicate customer's affinity for a plurality ofproducts.
 25. The computer software product of claim 17, wherein thestorage medium is located on an apparatus on a network remote from thecomputing apparatus.